One way to obtain an unbiased estimate of the ACE is to block (\(d\) -separate [1]}) the backdoor path \(T \leftarrow C \rightarrow Y\)  by conditioning on the value of \(C\)  during the analysis, e.g., by computing causal effect estimates separately for each level or stratum of \(C\)  and then combining them via a weighted average to obtain an estimate of the population ACE. Causal inference theory provides a number of results that characterize the sets of variables that may be used for confounding adjustment, in terms of the structure of a causal DAG. For example, the Backdoor Adjustment Theorem [1]} states that a set \(Z\)  of variables that blocks every backdoor path between the treatment variable and the outcome variable is sufficient for confounding adjustment.
